docstr: Create Iterator objects for multiple splits of a dataset.
  
  Args:
      datasets: Tuple of Dataset objects corresponding to the splits. The
          first such object should be the train set.
      batch_sizes: Tuple of batch sizes to use for the different splits,
          or None to use the same batch_size for all splits.
      Remaining keyword arguments: Passed to the constructor of the
          iterator class being used.
docstr_tok: ["Create","Iterator","objects","for","multiple","splits","of","a","dataset",".","Args",":","datasets",":","Tuple","of","Dataset","objects","corresponding","to","the","splits",".","The","first","such","object","should","be","the","train","set",".","batch_sizes",":","Tuple","of","batch","sizes","to","use","for","the","different","splits",",","or","None","to","use","the","same","batch_size","for","all","splits",".","Remaining","keyword","arguments",":","Passed","to","the","constructor","of","the","iterator","class","being","used","."]
code: def splits(cls, datasets, batch_sizes=None, **kwargs):
      """Create Iterator objects for multiple splits of a dataset.
  
      Args:
          datasets: Tuple of Dataset objects corresponding to the splits. The
              first such object should be the train set.
          batch_sizes: Tuple of batch sizes to use for the different splits,
              or None to use the same batch_size for all splits.
          Remaining keyword arguments: Passed to the constructor of the
              iterator class being used.
      """
      if batch_sizes is None:
          batch_sizes = [kwargs.pop("batch_size")] * len(datasets)
      ret = []
      for i in range(len(datasets)):
          ret.append(
              cls(
                  datasets[i],
                  train_info=datasets[0],
                  train=i == 0,
                  batch_size=batch_sizes[i],
                  **kwargs,
              )
          )
      return tuple(ret)
  
file_path: deepmatcher/deepmatcher/data/iterator.py
imports: from __future__ import division
  import logging
  from torchtext.legacy import data
  from ..batch import MatchingBatch
label_desc: Use image node_id 2 for calling the MatchingIterator obj's underlying member method code with example usage: obj.splits(cls, datasets, batch_sizes) and returns: tuple